Methods and apparatus for extended low contrast detectability for radiographic imaging systems

ABSTRACT

Evaluating dose performance of a radiographic imaging system with respect to image quality using a phantom, a channelized hotelling observer module as a model observer, and a printer, a plaque, or an electronic display includes scanning and producing images for a plurality of sections of the phantom using the radiographic imaging system, wherein the plurality of sections represent a range of patient sizes and doses and wherein the sections of the phantom contain objects of measurable detectability. Also included is analyzing the images to determine detectability results for one or more of the contained objects within the images of the plurality of sections of the phantom, wherein the analyzing includes using a channelized hotelling observer (CHO) module as a model observer; and displaying, via the printer, the plaque, or the electronic display, a continuous detectability performance measurement function using the determined detectability results.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.13/503,721, entitled “Methods and Apparatus for Extended Low ContrastDetectability for Radiographic Imaging Systems” by David P. Rohler etal., and filed Apr. 24, 2012, which itself claims priority to PCT PatentApplication No. PCT/US2010/002006, filed Jul. 16, 2010 and which claimsthe benefits of U.S. Provisional Patent Application No. 61/271,150 filedJul. 17, 2009 and U.S. Provisional Patent Application No. 61/278,954,filed on Oct. 14, 2009, all of which are hereby fully incorporated byreference.

BACKGROUND OF THE INVENTION

This invention relates generally to medical radiology and moreparticularly to methods and apparatus for radiographic imaging.

Radiographic imaging of all kinds, including computed tomography (CT)imaging, can detect small low contrast features. Thus, radiographicimaging has become important in medical practice, allowing medicalpractitioners to detect low contrast tumors and lesions in anatomicalregions of soft tissue, including the brain and the liver. An importantissue in radiology today concerns the reduction of radiation dosereceived by a patient during a CT examination without compromising imagequality. Generally, higher radiation doses result in the ability todetect lower contrast smaller objects, while lower doses lead toincreased image noise. Higher radiation doses also increase the risk ofradiation-induced cancer. Thus, the ability to image low contrastobjects at a low dose is desirable for diagnostic x-ray imaging methods.

The ability of a CT system to differentiate a low-contrast object fromits background is measured by its low contrast detectability (LCD). LCDis measured using phantoms that contain low-contrast objects of varioussizes. Phantoms that produce low contrast objects by using materialswith different densities are useful for testing conventional energyintegrating CT scanners. Phantoms that produce low contrast objectsusing energy sensitive materials allow performance testing for a dualenergy scanner.

The low-contrast resolution of a CT scanner is generally defined as thediameter of an object that is just detectable at a given contrast leveland dose. The contrast level is usually specified as a percentage of thelinear attenuation coefficient of water. A sample specification with thecurrent method might be “4 mm at 0.3% contrast for 10 mm slice thicknessat 30 mGy CTDIvol dose.” Sometimes other dose metrics are used, such asthe surface dose measured at the outer surface of the phantom or theSize Specific Dose Estimate {AAPM 2011}.

At least two LCD specifications are known. One known LCD specificationis made at a single protocol using human observation. In this method,reconstructed images are viewed by one or more human observers todetermine the smallest pin that, in the opinion of the observer, isvisible. Another known LCD specification is made at a single protocolusing a statistical method. In this method, an automated algorithmpredicts the contrast required to detect a given size pin with aspecified confidence interval from a flat “water” image.

These known LCD specifications characterize the performance of the CTscanner at only one protocol and one phantom size. Furthermore, theknown LCD specifications do not characterize the performance of a CTscanner over an extended range. For example, only a portion of the fulloperating range of the scanner is characterized. It would therefore bedesirable to provide methods and apparatus for characterizing theperformance of a radiometric imaging apparatus such as a CT scanner atmore than one protocol, over the full operating range of the imagingapparatus, or both.

Flux Index

At least some known commercial CT scanners operate over a wide range ofprotocols, each of which can have distinct contrast characteristics. Theprotocol parameters that affect contrast include scan time, tube current(mA), slice thickness, object diameter, tube voltage (kVp) and x-rayfilter. Contrast is also significantly affected by non-linearreconstruction methods as well as the reconstruction pixel size andreconstruction filter. It is assumed herein that the tube voltage, thex-ray filter, the scan diameter and the reconstruction method,collectively comprising a core operating mode, are fixed and that thescanner, in that core operating mode, can be characterized by theCTDIvol dose index. Then the parameters (example values of which aregiven in parentheses) that directly affect the x-ray flux available fordetection comprise scan time (0.25-2.0 sec/revolution), x-ray tubecurrent (20-400 mA), slice thickness (0.5-10.0 mm), object diameter(20-50 cm), and dose index (CTDIvol)

At least one known LCD method uses a CTP515 low contrast module of theCATPHAN® phantom, available from Phantom Laboratory, Inc., Salem, N.Y.“Supra-slice” contrast sets are used but only the lowest 0.3% contrastset is typically reported.

There are at least two LCD measurement methods known to be used oncommercial CT scanners. These methods are named the “human observermethod” and the “statistical method.” We have compiled some recentreported measurements from the major CT manufacturers and collected themin Table 1. [NHS Purchasing and Supply Agency, Buyer's Guide, ComputedTomography Scanners, Reports CEP08007, CEP08027, CEP08028].

TABLE 1 Recent Reported LCD Measurements from Major CT ManufacturersSource: NHS Purchasing and Supply Agency, Buyer's Guide, CT ScannersRef. Contrast Slice Num. on Index Scanner Contrast Pin Size DoseThickness mAs FIG. 1 Flux Index  500 A 0.3% 4 mm  10 mGy 10 mm 90 12 900 400 B 0.3% 5 mm  16 mGy 10 mm 180 14 1440 1000 C 0.3% 2 mm  40 mGy 10mm 350 16 3600  400 D 0.3% 5 mm 7.3 mGy 10 mm 105 18 657

These reported measurements show performance at only one point on theoperating curve and that the operating point is different for eachscanner, making performance comparisons invalid. This is shown as PriorArt FIG. 1 (based on it being based on previously reported measurements,not on it being presented an ExLCD graph) on an ExLCD graph 10 based ondefinitions of ExLCD Contrast Index and Flux Index described elsewhereherein.

Human Observer Method

In the Human Observer Method, LCD is determined by scanning a CATPHAN®phantom under selected protocol techniques and reconstructing the imageor images of phantom. One or more human observers are then presentedwith the image or images of the phantom to render an opinion regardingthe smallest object they believe is visible and therefore detectable forthe 0.3% contrast set. For the reported measurements described above, itis not clear to the inventors whether a single observer or multipleobservers were used. It is also not clear to the inventors how thespecific protocol was selected to derive the reported specification.

Statistical Method

The statistical method for LCD avoids problems associated with humanobservers by relying only on noise measurements in a reconstruction. Itdoes not use a phantom with actual contrast objects. Instead, itanalyzes image noise in a specific manner that determines the amount ofcontrast needed to detect an object of a given diameter relative to thebackground with a stated level of confidence. Because the assessment ismade by the computer and not a human observer, the method is repeatableand reproducible. However, the statistical method cannot differentiatecontrast performance resulting from non-linear reconstruction methodssince only a noise image is evaluated. The performance of the systemrelative to how well the original low contrast object is preserved thuscannot be determined, as is true of any noise analysis method that doesnot measure an actual object.

Quantum Noise Limited

An imaging system is said to be “quantum noise limited” if, for allpractical purposes, the only source of image noise is the statistics offinite x-ray quanta and electronic noise is absent. Referring to graph20 of prior art FIG. 2, the S/N (signal to noise) ratio is plotted as afunction of relative x-ray Flux Index. In a log-log plot, the S/N ratiotrace 22 for a quantum noise limited system is represented by a straightline having a slope of ½. If electronic noise (also known as “systemnoise”) is present, the overall S/N is significantly affected only forlower flux values as shown by trace 24 in FIG. 2.

With at least one LCD method known by the inventors to be in currentuse, a scanner is characterized with only one contrast measurement takenat a single protocol. This single measurement does not adequatelycharacterize the contrast performance of the scanner. The singleprotocol measurement implies a contrast performance that follows aquantum noise limited curve defined by the single measurement. There isthus an inadequacy of the single protocol contrast performance curve.Additionally, this known LCD method does not adequately handle smallerpins that are affected by system blurring, i.e. the Modulation TransferFunction (MTF).

At least some known detectability methods that are based only on a noiseanalysis (such as the statistical method, noise power spectrum,simple-pixel standard deviation, and matched filter standard deviation)can overestimate the performance of a reconstruction process that altersthe contrast of the test object. These known detectability methods usereconstruction processes that limit spatial bandwidth of both noise andobject and do not account for changes in the assumed object. Forexample, assume that a small pin in an LCD test phantom is a cylinderwith a 2 mm diameter and a contrast of 0.3%. If perfectly reconstructed,image pixels within the area of the pin have an average contrast of 0.3%and all pixels outside this region have an average contrast of 0%.However, the MTF of the system will blur the pin (especially at itsedges) and spread some of its contrast into pixels beyond the originalgeometric boundary, resulting in a reduction in average contrast withinthe pin region.

Thus, it will be understood that inaccuracies of at least some knownsingle protocol LCD methods result from human observer variation, finitepin size selections, selection of protocol, presence of system(electronic) noise; and/or system blurring (MTF) of smaller pins

The low contrast detectability (LCD) performance of a CT system is acritical performance characteristic, providing a measure of the abilityof a scanner to produce high quality images at a low x-ray dose such asthe lowest possible x-ray dose. Because the use of lower dose protocolsin CT scanners is now of considerable importance, it is correspondinglydesirable for LCD to be measurable over a wide range of protocols andbody sizes. However, inaccuracies of the known prior art effectivelyprevent true differentiation of the contrast performance between CTscanners.

Automatic Exposure Control (AEC) systems for radiographic imagingsystems such as CT are known to be in widespread clinical use. Anobjective of these systems is to reduce patient dose by allowing the CTsystem to determine and modulate an mA along a patient's Z axis asnecessary to achieve a desired Clinical Image Quality (CIQ). A userdetermines or selects a CIQ necessary or desirable for the clinicalapplication in terms of an Image Quality Metric (IQM) goal parameterprovided by the CT vendor and the CT system is designed to produce theappropriate x-ray dose to achieve it. XY or angular modulation is alsoprovided in at least some known CT systems, but AEC as used hereinrefers to Z axis modulation.

An important consideration for an AEC system is how the user specifies adesired CIQ. Depending upon the CT vendor, some known CT systems use avariety of IQMs. These methods include specifying a reference mA basedon an nominal patient size chosen by the vendor, an image standarddeviation, a noise index, or a reference image. However, methods knownby the inventor to be in current use do not adequately describe CIQ, arenot universal (i.e., the same values cannot be used on other make andmodel scanners), and may not track the desired CIQ with patient size. Inaddition, the use of different methods to determine an IQM increasesconfusion among technologists, increasing the likelihood of medicalerrors as well as making it more difficult to compare IQ and dosetradeoffs for different features and systems.

Size and contrast of an object, such as a lesion, that can besuccessfully identified with adequate sensitivity and specificity dependon many factors {Barrett 2004}. Object detectability is a significantcomponent of clinical image quality and is related to dose applied andthe image generation method used. It is well known that objects are moredifficult to successfully identify as noise increases. Image noise ischaracterized as a mottle of pixel variations without any apparentconsistent structure. CT image noise results from x-ray quanta as wellas non-quantum sources. X-ray quantum noise is statistical photon noisethat decreases inversely with the square root of the X-ray intensity,which in turn is proportional to the mA selection. Non-quantum noiseincludes electronic and electromagnetic sources and generally becomes anoticeable factor only at low x-ray flux levels with large patients.However, noise alone does not determine detectability, which is alsoinfluenced by how well an image generation system reproduces a scannedobject within an image. The reproduction of the object is especiallyimportant when evaluating adaptive and model-based iterative imagegeneration methods. Thus, an IQM based on detectability is better ableto universally describe patient CIQ goals. The IQM goal metrics used byat least some known CT AEC systems are not universal.

SUMMARY OF THE INVENTION

In one aspect, some embodiments of the present invention thereforeprovide a method for a method for evaluating dose performance of aradiographic imaging system with respect to image quality using aphantom, a channelized hotelling observer module as a model observer,and a printer, a plaque, or an electronic display. The method includesscanning and producing images for a plurality of sections of the phantomusing the radiographic imaging system, wherein the plurality of sectionsrepresent a range of patient sizes and doses and wherein the sections ofthe phantom contain objects of measurable detectability. Also includedis analyzing the images to determine detectability results for one ormore of the contained objects within the images of the plurality ofsections of the phantom, wherein the analyzing includes using achannelized hotelling observer (CHO) module as a model observer; anddisplaying, via the printer, the plaque, or the electronic display, acontinuous detectability performance measurement function using thedetermined detectability results.

In another aspect, some embodiments of the present invention provide aphantom for use with radiographic imaging systems. The phantom has oneor more sections, wherein each of the sections further includes aplurality of cross-sectional areas that have: a region having objects tobe detected by the radiographic imaging system; a background region withno objects; and regions having densities matching objects to be detectedand that are sufficiently large so as to enable the measurement ofeffective contrasts of the objects to be detected.

In yet another aspect, some embodiments of the present invention providea method for setting a protocol for imaging a patient using acomputerized radiographic imaging device. The method includes imaging aphantom containing a plurality of objects using a plurality of fluxsettings within an operating range for at least one operating protocolof the computerized radiographic imaging device to obtain projectiondata. The method also includes reconstructing the projection data into aplurality of reconstructed images of the phantom corresponding to theplurality of flux settings using the radiographic imaging apparatus.Also, for each of the flux settings, the method includes, with thecomputerized radiographic imaging apparatus: automatically calculating adetectability of the objects in a reconstructed image corresponding tothe flux setting; selecting the automatically calculated detectableobjects in accordance with a detectability criterion; determining acontrast measure for the selected objects; and associating a contrastperformance with the flux setting of the image in accordance with thedetermined contrast measures. The method further includes imaging thepatient with the computerized radiometric imaging device using aradiation dose in accordance with the associated contrast performanceand flux settings to produce an image of the patient having a desiredimage quality.

In yet another aspect, some embodiments of the present invention includea method of determining an extended low contrast detectabilityperformance function as a relation between a flux index and a contrastindex for an operating range for a core operating mode of a radiographicimaging system using actual reconstructed images. This method includesselecting a plurality of protocols distributed across the operatingrange of the radiographic imaging system and imaging a phantomcontaining a plurality of objects over each of the protocols. The methodfurther includes computing a detectability for each object in order todetermine a relative flux and contrast index set of ordered pairs foreach object and determining a smallest detectable object size for eachcontrast set. Also included in the method is computing the contrastindex for each protocol for each contrast set; and utilizing the orderedpairs of flux index and contrast index to determine the extended lowcontrast detectability performance function for the radiographic imagingsystem.

These and other aspects of the disclosure and related inventions arefurther described herein with reference to the accompanying Figures.

It will be appreciated that some embodiments of the present inventionprovide at least one or more desirable features, among which may includecharacterization of the performance of a radiometric imaging apparatussuch as a CT scanner at more than one protocol, over a full operatingrange of the imaging apparatus, or both. Also included may be theadequate handling of smaller pins that are affected by system blurringand/or remedying of the inadequacy of a single protocol contrastperformance curve. Also included may be the remedying of inaccuraciesthat prevent true differentiation of contrast performance betweendifferent CT scanners, an adequate description of CIQ, a universaldescription of CIQ, and the tracking of desired CIQ with patient size.In addition, some advantages that may be realized include less confusionamong technologists, and a better way to determine detectability inradiometric imaging systems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a prior art graph of measurements from major CT manufacturers;

FIG. 2 is a graph of a prior art signal-to-noise ratio plotted as afunction of relative x-ray Flux Index.

FIG. 3 is a shaded 3-D drawing of a phantom embodiment.

FIG. 4 is a cross sectional view through a section of the phantom shownin FIG. 3 showing low contrast objects embedded therein.

FIG. 5 is a cross sectional view through a section of the phantom shownin FIG. 3 showing large regions for low contrast measurements.

FIG. 6 is a cross sectional view through a section of the phantom shownin FIG. 3 showing a uniform region in which objects are absent.

FIG. 7 is a example graph of an Image Quality (IQM) function calledContrast Index.

FIG. 8 is a drawing of a representative data flow for a processembodiment for protocol selection using an ExLCD Performance Function.

FIG. 9 is a graph illustrating the standardization of clinical protocolsbetween two or more scanners.

FIG. 10 is a schematic representation of a patient image.

FIG. 11 is a schematic representation of projection data obtained from apatient.

FIG. 12 is a flow chart of an embodiment of an ExLCD method.

FIG. 13 is a drawing of an ExLCD contrast performance curve derived fromcontrast measurements of a typical simulated CT scanner.

FIG. 14 is a graph illustrating qualitatively how a contrast performancecurve is affected by some system characteristics.

FIG. 15 is a graph illustrating pin size sampling and contrast setsampling.

FIG. 16 is a see-through perspective view of an ExLCD phantomembodiment.

FIG. 17 is a view of simulated cross sections of different diametersections of the phantom shown in FIG. 16.

FIG. 18 is a two-dimensional cross-sectional view of an alternativeembodiment of an ExLCD phantom.

FIG. 19 is a see-through perspective view of another alternativeembodiment of an ExLCD phantom.

FIG. 20 is a see-through perspective view of yet another alternativeembodiment of an ExLCD phantom.

FIG. 21 is a example of a graph of contrast sets represented at eachprotocol of a CT scanner.

FIG. 22 is an illustration of a result of one embodiment of thedetectability determination for a reconstructed image slice described asTest 32 in Table 4.

FIG. 23 is an example graph of a Contrast Performance Curve that can bedetermined by a least squares fitting of ordered pairs to a curve.

FIG. 24 is an ExLCD graph showing directions of better image quality,lower technique, larger patients, smaller objects (lower contrast) andlarger objects (higher contrast).

FIG. 25 is a pictorial schematic chart showing an ExLCD detectabilityembodiment.

FIG. 26 is an ExLCD contrast measure graph.

FIG. 27 is a plot of selected detectability values for a Rose Criterionvisibility index.

FIG. 28 is a plot of an ExLCD contrast index corresponding to the plotof FIG. 27.

FIG. 29 is a schematic illustration of a matched filter detectabilityanalysis method.

FIG. 30 is a plot showing order pairs of [Flux Index, Contrast Index].

FIG. 31 is a contrast performance curve generated in one embodiment by aregression fit to a 2-parameter equation.

FIG. 32 is a plot showing the comparison of contrast performance curvesfor three scanners.

FIG. 33 is a graph showing contrast reduction due to pin blurring in ahead scan protocol.

FIG. 34 is a graph showing pin blurring in a body scan protocol.

FIG. 35 is a graph showing a large pin contrast curve (pins>2.5 mm).

FIG. 36 is a graph showing small pin contrast performance curves (pinsof 2.5 mm and 2 mm).

FIG. 37 is a flow chart illustrating the steps needed in one embodimentto convert desired CIQ into a protocol recommendation for a scanner.

FIG. 38 is a plot of a Channelized Hotelling Observer output of signalto noise performance for each instance of a pin size and contrast for anexample phantom.

FIG. 39 is a schematic chart showing how image quality results arereplicated between more than one scanner.

FIG. 40 is a graph of a family of performance functions for an automaticexposure control mode of a CT scanner.

FIG. 41 is a schematic representation of an embodiment in which acollection of SNR values for a first scanner are translated to a second,different scanner and an associated collection of FluxIndex values arecombined to provide a desired FluxIndex and associated protocol settingsfor scanning a patient.

FIG. 42 is a drawing of a graph indicating how an aggregate SNR functionis generated.

FIG. 43 is a schematic representation of an embodiment in whichcombinations of multiple object instances are used to find a FluxIndexrequired for each pin contrast in order to achieve a specified SNR.

FIG. 44 is a graph representing an embodiment in which multiple pindiameters are analyzed using CHO, mapped to a Contrast Index andcombined using a weighted mean.

FIG. 45 is a graphical representation of an embodiment in whichstatistical distribution information from a CHO ROC curve is used todetermine detectability.

FIG. 46 is a representation of alternate presentations of SNR vs.FluxIndex information in various embodiments.

FIG. 47 is a graph illustrating that embodiments of ExLCD methods andapparatus can be enhanced by using all object sizes for a given contrastlevel to determine a smallest detectable object.

FIG. 48 is a graphical flowchart of an ExLCD embodiment in which CHO isembedded in a CT scanner.

FIG. 49 is a graphical flowchart of an ExLCD embodiment in which ExLCDis provided as an external advisor to a CT scanner.

The foregoing summary, as well as the following detailed description ofcertain embodiments of the present invention, will be better understoodwhen read in conjunction with the appended drawings. To the extent thatthe figures illustrate diagrams of the functional blocks of variousembodiments, the functional blocks are not necessarily indicative of thedivision between hardware circuitry.

DETAILED DESCRIPTION OF THE INVENTION

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralsaid elements or steps, unless such exclusion is explicitly stated.Furthermore, references to “one embodiment” of the present invention arenot intended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features. Moreover, unlessexplicitly stated to the contrary, embodiments “comprising” or “having”an element or a plurality of elements having a particular property mayinclude additional such elements not having that property.

The embodiments recited herein are described in the context of computedtomography (CT) applications, but the inventive technology describedherein is not limited to CT and can be applied to other radiographicimaging systems as well. Thus, the use of the terms “CT” and “CTscanner” should be interpreted as also encompassing other radiographicimaging systems, unless otherwise stated.

As referred to herein, a “radiographic imaging system” is an imagingsystem that uses electromagnetic radiation (x-ray, gamma ray, etc.) forbuilding an image. For example, the radiographic imaging system useselectromagnetic radiation of such short wavelength that it is able toproduce an image showing internal structures of an object, such asorgans in a patient's body. Examples of radiographic imaging systemssuitable for use in or in conjunction with embodiments of the presentinvention include but are not limited to CT scanners, digitalradiographic devices, mammography devices, nuclear imaging devices, andSPECT devices.

Unless otherwise indicated, the contrast measurements discussed hereinare indicative of contrast at the center of an object such as a patient.For that reason, the descriptions of ExLCD methods herein are based onthe relative flux index.

Technical results of various configurations and embodiments of thepresent invention include one or more of the following: characterizationof the performance of a radiometric imaging apparatus such as a CTscanner at more than one protocol, over a full operating range of theimaging apparatus, or both; the adequate handling of smaller pins thatare affected by system blurring and/or remedying of the inadequacy of asingle protocol contrast performance curve; the remedying ofinaccuracies that prevent true differentiation of contrast performancebetween different CT scanners, an adequate description of CIQ, auniversal description of CIQ, and the tracking of desired CIQ withpatient size; less confusion among technologists, and a better methodand apparatus to determine detectability in radiometric imaging systems.

“ExLCD” is a method for generating a continuous image quality function(Contrast Index vs. Flux Index) that provides a metric to relate thedetectability in radiological images of small low contrast objects tothe technique used when acquiring the images. ExLCD provides an imagequality metric (IQM) used in some embodiments to guide clinical practiceregarding appropriate clinical image quality (CIQ) and associated doseutilization on a radiological imaging device (scanner), or moreuniversally, on a plurality of such devices. ExLCD also allows thequantification of image quality and dose performance for differentscanners or operating modes to be compared on a common scale over theperformance range continuum.

In at least one embodiment and referring now to FIG. 3, ExLCD usesimages produced by scanning a phantom such as specially designed phantom300 to measure scanner performance. Phantom 300, for example, comprisesa plurality of sections 302, 304, 306, 308 that have diametersrepresenting a range of patient sizes. Referring now to FIG. 4, eachsection of a given diameter, for example, section 304, contains smalllow contrast objects 402 (such as rods) of various sizes and contrastlevels. In some embodiments and referring to FIG. 5, sections such assection 304 contain large regions 502 for low contrast measurements.Also, in some embodiments and referring to FIG. 6, sections such assection 304 contain uniform regions 602 in which objects 402 are absent.To characterize a scanner, phantom 300 is scanned over a range of FluxIndex settings and the resulting data is reconstructed to produce aplurality of images. A detectability module analyzes low contrastobjects in the images to measure detectability to produce an ImageQuality (IQM) function called Contrast Index as shown in graph 700 ofFIG. 7.

In one embodiment, the ContrastIndex value is written

$\begin{matrix}{{ContrastIndex} = \frac{6000}{{pinSize} \times {Contrast}}} & (1)\end{matrix}$

and is a set of measurements of a smallest detectable pin 402 at eachcontrast value 702. The value of FluxIndex is written:

$\begin{matrix}{{FluxIndex} = {{mAs} \times {slice} \times {\frac{e^{{- {Diam}} \times \mu_{water}}}{e^{{- {Diam}_{ref}} \times \mu_{water}}}.}}} & (2)\end{matrix}$

(Note: Diam is also written as D_(weq).)

Trace 704 in FIG. 7 is the result of a regression model fittingContrastIndex as a function of FluxIndex.

Referring now to FIG. 8, a representative data flow 800 is shown for aprocess embodiment for protocol selection using an ExLCD PerformanceFunction 802. ExLCD Performance Function 802 is provided for aparticular CT scanner (or, more generally, a particular radiographicimaging system). A radiologist (or other operator or responsible party)at block 804 selects a desired contrast level in accordance withclinical image quality (CIQ) requirements. (In FIG. 8, the termContrastMeasure is synonymous with ContrastIndex.) In this example, thedesired ContrastIndex is computed at block 806 from the inputparameters, written:

$\begin{matrix}{M = {\frac{M_{0}}{cp} = {\frac{6000}{(40)(5)} = 30.0}}} & (3)\end{matrix}$

corresponding to a 5 mm pin at a contrast level of 40 Hounsfeld Units(HU). A Protocol Selection module at block 808 then determines a FluxIndex value on or near a Contrast Performance Curve 802 corresponding tothe desired ContrastIndex. Since the ExLCD Performance Function isalways monotonic, there will be a unique optimum Flux Index valuecorresponding to the intersection of the Contrast Index value and theExLCD Performance Function. For this example, the Flux Index valuedetermined is 3.8. This unique Flux Index value is used in thisembodiment to determine the optimal protocol at block 810, which, inthis embodiment, uses the relationship written

$\begin{matrix}{{mAs} = {{\frac{FluxIndex}{SliceThick}e^{{({D - D_{ref}})} \times \mu_{water}}} = 41.5}} & (4)\end{matrix}$

where D_(ref)=20 and μ_(water)=0.2. The mAs is then output or sent to ascanner to perform the procedure at block 812.

(Although not explicitly stated for each embodiment and configurationdisclosed herein, embodiments in which approximations are used toapproach an optimum protocol or to at least reduce or otherwise optimizeradiation dosage are also useful and are considered by the inventors tofall within the scope of the present invention.)

In at least one embodiment of the present invention, slice thickness isselected as an independent parameter and a method for determining thepatient body diameter D is used. A method suitable for such use is aprior art method described below for determining the patient waterequivalent diameter (D_(weq)). For the example illustrated in FIG. 8,the patient diameter is 40 cm and the slice thickness is 5 mm, resultingin the determination of mAs=41.5 as the optimal flux for the desiredimage quality.

In some embodiments and referring to graph 900 of FIG. 9, clinicalprotocols are standardized between two or more scanners. Assuming thatthe “red scanner” is the baseline scanner for which protocols have beendeveloped, this method determines corresponding clinical protocols forthe “blue scanner.” The method includes steps of, for each desiredprotocol on the red scanner, (a) determining, at block 902, acorresponding Flux Index for the red scanner protocol, (b) looking up,at block 904, a corresponding Contrast Index on the red scanner's ExLCDPerformance Function, (c) finding, at block 906, an equivalent ContrastIndex value on a blue scanner's ExLCD Performance Function, and (d)looking up, at block 908, a corresponding FluxIndex in accordance withon the blue scanner's ExLCD Performance Function, thus determining anequivalent clinical protocol for the blue scanner.

Patient Water Equivalent Diameter

Referring now to FIGS. 10 and 11, the overall attenuation of a scannedobject, such as patient 1100, can be determined from a CT image 1000from projections 1102 in terms of a water equivalent area. The summationof I(x, y) is the water equivalent area, where I(x, y) is obtained fromimage pixels of the CT image, converted to an area weighted by therelative attenuation of the pixels. The square root of the waterequivalent area is defined as Attenuation Units (AU).

D _(weq)=2×√{square root over (ΣI(x,y)/π)}  (5)

and

I(x,y)=(image(x,y)/100+1)×PixelArea  (6)

Water equivalent diameter can also be estimated from a scan projectionradiograph using the projection area and an appropriate scannerdependent conversion factor, for example, 0.557, for a commerciallyavailable multi-slice scanner.

D _(weq)=2×0.557×√{square root over (ΣP(i)/π)}  (7)

In some embodiments of the present invention, patient information isobtained using boundaries of a body shown in a radiograph. In someconfigurations of this embodiment, Body Mass Index along with bodydiameter are used to optimize protocols. The value for mAs(milliAmpere-seconds) of dose is then written:

$\begin{matrix}{{mAs} = {\frac{FluxIndex}{sliceThick}e^{{({D_{weq} - D_{ref}})} \times \mu_{water}}}} & (8)\end{matrix}$

where D_(weq) is the effective water equivalent object diameter, andD_(ref) is the effective water equivalent diameter of a referenceobject. Knowing the Flux Index, since the patient diameter D_(weq) isknown along with the slice thickness sliceThick, a required mAs for thescan is thus determined to achieve the desired CIQ for the patient. (Aswill be understood by those skilled in the art, a “required” mAs valueneed not be exact, but actually encompasses a range of values withinengineering and medical tolerances that produce essentially similarresults. Therefore, when a single “optimum” or “required” value isrecited hereinafter, it will be understood to encompass not only theoptimum or required value stated, but also a range of values withinthese tolerances unless explicitly stated otherwise.)

In some embodiments, both the object diameter and μ_(water) aredetermined using a water beam hardening corrected mean amplitude (e.g.,mean of the highest 50 samples) of scan projection radiograph from anorientation with the longest path length (usually the lateraldirection). Because image noise is generally most influenced by thenoisiest projections, these embodiments can provide more consistentcontrast performance than those using D_(weq) determined from the waterequivalent area.

In some embodiments of the present invention, a particular scanner hasmore than one ExLCD Performance Function. For example, a scanner mayhave an ExLCD Performance Function for each of:

1. Slice thickness;2. X-ray beam energy (including dual energy);3. Choice of reconstruction method (examples of such choices includefilter choice and degrees of inclusions of non-linear reconstructionalgorithms); and4. Selection of compensator.Therefore, some embodiments use a plurality of ExLCD PerformanceFunctions dependent upon the protocol parameters that are provided asinput.

Some embodiments of the present invention provide a method fordetermining a desired Contrast Index in a clinical setting. For example,one embodiment accepts as input a specification of a desired objectcontrast differentiation in Hounsfield units and an object size.

Also, in some embodiments, a desired Contrast Index is derived usingactual clinical images in a clinical setting. For example, clinicalimages from various patients at various dose levels for a particularclinical task on an ExLCD calibrated scanner are qualitatively graded byradiologists for acceptability. ExLCD is then used to determine thecontrast index for each patient image. In this way, clinical opinionsare associated with the ExLCD performance relationship, and inparticular, this association relates the IQM to CIQ. A sufficient numberof qualitative radiologist studies regarding clinical acceptability isused to determine an appropriate contrast index to use in clinicalpractice. The use of an ExLCD performance relationship provides theability to reproduce required results for any patient on any calibratedscanner.

It is advantageous from the standpoint of possible patient side effectsto use the smallest possible concentration of contrast media, however,it is also important to use enough contrast so that the desired CIQ canbe achieved. The use of ExLCD performance relationships in someembodiments is thus expanded to optimize a concentration of contrastmedia used for a radiographic imaging system.

In some embodiments, to track contrast performance that is affected bynon-linear or iterative reconstruction, the reconstructed objectcontrast is measured. Using measured contrast, a reconstruction processembodiment with a highly filtered noise spectrum causes object smoothingthat results in a lower ExLCD Contrast Index than a reconstructionprocess that is able to filter the noise while retaining the spatialgeometry of the original object.

In some embodiments of methods using ExLCD technology, at least fourcomponents are used:

(a) an ExLCD phantom 300 containing various contrast/diametercross-sections;(b) a set of scan protocols and image slices used for ExLCD measurement;(c) a detectability determination module; and(d) a Contrast Index function generator and parameter calculation moduleor modules.

In some embodiments and referring to flow chart 1200 in FIG. 12, a setof image slices 1202 at varying flux levels is obtained by scanning anExLCD phantom 300. For each image 1 . . . N of image slice set 1202, aselection process 1204 is performed. Selection process 1204 includes, atblock 1206, computing a detectability for each pin 402 of phantom 300,selecting pins 402 that meet a detectability criterion at block 1208,and computing a contrast measure at block 1210 for the pins selected atblock 1208. The computed contrast measures for the set of images 1202are used by a contrast performance curve module to generate or compute,at block 1212, a contrast performance curve graph 1214. Contrastperformance curve graph 1214 is provided to a user in a tangible form insome embodiments. This form may be, for example, a printed graph. Insome embodiments, it is provided electronically, such as in a ROM, aRAM, a DVD, a CD, or in some other electronically readable (includingelectronic computer optics and magnetics) form and may be storedpermanently (or in some embodiments, erasably) thereon or therein. Insome embodiments, the form may be a hard magnetic disk drive or othermemory. In some embodiments, graph 1214 is provided in a form that is orcan be stored in a memory unit of the radiographic scanner used togenerate the curve.

As used herein, the terms “extended low contrast detectability” and“ExLCD” refer to a performance relationship for a radiographic imagingsystem that provides numeric LCD values (Contrast Index) over a range ofoperating conditions and patient sizes (Flux Index). The terms “extendedlow contrast detectability function”, “ExLCD function” and “ExLCDperformance function” and “ExLCD performance curve” and “ContrastPerformance Curve” refer to a data representation or to a tangiblerepresentation of the Contrast Index vs. Flux Index relationship.

Referring now to FIG. 13, an ExLCD contrast performance curve 1300 for atypical (simulated) CT scanner is shown along with a set of contrastmeasurements 1302 made over an entire flux range of the scanner. (Notall contrast measurements 1302 are labeled in FIG. 13.) CT systems varyin their contrast performance based on system characteristics that caninclude overall dose/quantum efficiency, system/electronic noise, systemblurring (MTF), and/or implementation of non-linear reconstructionmethods.

Referring now to graph 1400 of FIG. 14, dotted and dashed traces 1402and 1404, respectively, illustrate qualitatively how the contrastperformance curve is affected by some of these system characteristics.In graph 1400, dotted line 1402 represents a radiographic system havinga high quantum efficiency. Dashed trace 1404 represents a radiographicimaging system having low system noise and/or improved non-linearreconstruction. Solid trace 1406 represents a baseline contrastperformance curve. Each trace 1402, 1404, and 1406 represents ahypothetical ExLCD performance curve that might be representative of adifferent physical radiographic imaging system. Line 1408 is a linedrawn at a constant detectability of 10.0. At this contrastdetectability, the intersections of traces 1402, 1404, and 1406 withline 1408 show that the high quantum efficiency system can deliver animage contrast index of 10.0 with a dose 2.5 times lower than thebaseline system, and the system having lower noise and/or improvednon-linear reconstruction at a dose 6.0 times less than the baselinesystem.

In some embodiments, a FluxIndex value is defined for each protocolvariation within a core operating mode to incorporate those protocolparameters that affect the x-ray flux available for detection or imageperformance reconstruction parameters. The Flux Index value is“relative” to the core operating mode in that a Flux Index value for onecore operating mode cannot be directly compared to a Flux Index valuefor another core operating mode. The relative Flux Index value, for aspecific core operating mode, is any expression that is proportional tothe x-ray flux available for detection. By way of example, for a CTscanner, a possible definition is written as in Equation (9) below andas explained in the accompanying descriptions.

Contrast Index

A relative flux measure, designated as the “flux index,” incorporatesthese five parameters as written in Equations (9):

$\begin{matrix}\begin{Bmatrix}{{\begin{matrix}{{FluxIndex} = {\quad{\quad{\frac{CTDIvol}{{CTDIvol}_{ref}}{\quad\quad} \times}}}} \\{({mA}) \times {\quad{\quad{({sliceThick}) \times {\quad\quad}({scanTime}){\quad{\times \frac{e^{{- {({objDiam})}} \times {({attWater})}}}{e^{{- {({refDiam})}} \times {({attWater})}}}}}}}}}\end{matrix}\quad}\quad} \\{{refDiam} = {20.0\mspace{14mu} {cm}}}\end{Bmatrix} & (9)\end{matrix}$

CTDIvol is per 100 mAs and CTDIvol_(ref) is an arbitrary constant dosereference value per 100 mAs that will be determined for each coreoperating mode tested. The CTDIvol ratio is optional in Equation (9)because it is included to normalize flux index for making dosecomparisons. For practical combinations of these parameters, the rangeof FluxIndex is approximately [0.1, 7,000.0]. An example of a currentLCD specification could be “4 mm at 0.3% for 10 mm slice at 90 mAs.”Because this example relates to the 20 cm CATPHAN® phantom, FluxIndexwould be 900.

The relative FluxIndex described above relates linearly to dose exceptfor the factor involving the object diameter. The currently accepteddose index for CT is CTDIvol as defined in IEC 60601-2-44. Dose islinearly related to flux for a given object size and slice thickness.

To extend the measurement of low contrast detectability, someembodiments use a new contrast measure M. This contrast measure iswritten as:

$\begin{matrix}{M = \frac{M_{0}}{cp}} & (10)\end{matrix}$

and is designated as the “contrast index.” In Equation (10), p is thesmallest pin size, measured in millimeters, visible at contrast level c,measured in Hounsfield units (HU) where one Hounsfield unit correspondsto 0.1% of water attenuation, and M₀ is an arbitrary constant forbringing the measure M into a convenient numerical range. It isimportant to note that contrast level c in this definition is thenominal or expected contrast level of the object as opposed to ameasured contrast level, which is later indicated with an upper case C.(In another embodiment, p is the diameter of a pin and c is the contrastat which a pin of that diameter is determined to be detectable.) In thisexample, M₀=6000 is used to map the best current contrast specificationof 2 mm at 0.3% to a contrast measure of 1000. For example, thespecification, “4 mm at 0.3% contrast for 10 mm slice thickness at 30mGy CTDIvol,” would generate a contrast measure of 500 written as

$\begin{matrix}{M = {\frac{6000}{(3)(4)} = 500.}} & (11)\end{matrix}$

In other embodiments, the contrast index is obtained by applying athreshold to the SNR calculation for detectability from CHO, NPWMF, etc.And in yet other embodiments, the SNR itself is used for detectability.

In some embodiments, a Contrast Index value is written in Equation (10)as is described in the accompanying descriptions. For example, aContrast Index is determined by measurement and calculation for eachprotocol within any core operating mode and for each relevant contrastset. For a given core operating mode, each set of contrast objects isassigned a nominal contrast level, c, that is set by the manufacturingcharacteristics of the phantom as determined by the phantom design andthe phantom calibration done for the core operating mode. Thedetectablity p and contrast c of each detectable object size is thendetermined for each protocol within the core operating mode. Asdescribed elsewhere herein, in some embodiments, a detectability valuefor each object size in the contrast set is determined by examining theimage(s) produced for that protocol and then determining a smallestobject size, p, that corresponds to a detectability value that isgreater than or equal to the detectability threshold.

A contrast set is relevant for a given set of protocol parameters ifeither some but not all objects in the set are detectable. In someembodiments of the present invention, the detectablity of an object isreliably determined by extrapolation or interpolation from thedetectability measures of the objects in the contrast set.

In some embodiments, a plurality of calibrations for a givenradiographic imaging system is performed. In one example, a completeExLCD Scanner Characterization includes the following steps:

(a) A new ExLCD Calibration is performed for each core operating mode.The core operating mode changes when changes are made in core operatingparameters, e.g.

-   -   (1) X-ray tube energy;    -   (2) Source filter and collimator; and/or    -   (3) Reconstruction mode, e.g. non-linear reconstruction;        (b) Up-to-date dose measurement; and        (c) Calibration of the ExLCD Phantom to compensate for        manufacturing tolerances and scanner spectral characteristics.

As referred to herein, an ExLCD performance curve or ExLCD performancefunction is one form or format of an output of an embodiment of thepresent invention for a core operating mode for the radiographic imagingsystem. The ExLCD performance curve is indicative of a relation betweenthe Flux Index and the Contrast Index over a range of the Flux Index forthat core operating mode. In some embodiments, the ExLCD performancefunction is represented as an array of Flux Index and Contrast Indexvalues or by another appropriate parameterization. In some embodiments,the relation is provided in a form that provides a capability (e.g., anon-line capability) to determine a Contrast Index for any desired FluxIndex or conversely to determine the protocol parameters for any desiredContrast Index and any patient size.

Some embodiments of the present invention include apparatus and/ormethods for ascertaining the quality of an image interpretation task.Some of these apparatus and/or methods include one or more of humanopinions of object visual quality in fixed object phantoms (poorest ofmethods), human task based observations regarding how accurately thepresence or absence of an object in an image can be determined (forcedalternative choice methods, for example), statistical noise analysismethods whereby the detectability of an object is inferred using somemeasure of image noise, matched filter methods whereby object spatialfrequencies are determined and then used to analyze noise within thosespatial frequencies, an ideal Bayesian Observer signal to noiseanalysis, a Non Pre-whitening Matched Filter signal to noise ratio(NPWMFSNR), etc. Methods and apparatus recited in this paragraph aredescribed, for example, by the International Commission on RadiationUnits and Measurements (ICRU) Report 54 “Medical Imaging—The Assessmentof Image Quality”, wherein is incorporated herein by reference. NPWMFSNRhas been found to most closely represent objective human task basedassessments. The NPWMFSNR is therefore used in some embodiments of thepresent invention although other methods are employed in otherembodiments. In some embodiments of the present invention, a variationof the NPWMFSNR that measures a reduction in contrast of the object dueto the MTF of the system is used. In some of these embodiments, systemsthat reduce the spatial frequencies of the noise but retain the spatialfrequencies of the input object will score a higher NPWMFSNR.

Dual Energy

Embodiments of the present invention can be used in energydiscriminating radiographic imaging in a manner similar to that used inenergy integrating imaging with some modifications. For example, in someembodiments, objects within a phantom used for calibration comprise anenergy sensitive material such as calcium hydroxyapatite. The phantomobjects comprise various percentages of the energy sensitive material toallow concentration sets of energy sensitive material objects to bebuilt, thereby making the phantom objects sensitive to the energydiscrimination acquisition and reconstruction methods employed by theradiological imaging device.

Energy discriminating systems can provide various types of images. Forexample, for dual energy CT, these images may, in some embodiments,include high kV and low kV images that are comparable to conventionalimages. In some embodiments, a set of basis material images such as acalcium image and water image (if the basis materials chosen are calciumand water) are included. Also in some embodiments, monochromatic imagesat a selected keV that are produced by an appropriate combination ofdata from the basis material images or basis material projection dataare included. One or a plurality of such types of images is evaluated bymethods employing ExLCD using an energy sensitive phantom in someembodiments.

ExLCD Phantom

In some embodiments of the present invention, an ExLCD phantom 300, suchas the one best seen in FIGS. 3, 4, 5, and 6, is used to make contrastmeasurements over the flux range. For example, in some embodiments, aphantom diameter of 20 cm is used to support flux values at the highflux end of the desired range. To achieve the lowest flux values in adesired range with appropriate scan parameters, a second phantomdiameter of 40 cm is provided.

When the detected flux is at the lower end of the desired range, thecontrast levels in at least one known CATPHAN® will not be seen.Therefore, additional contrast sets are introduced to be detectable inthe low flux ranges.

In at least one ExLCD phantom embodiment and referring to FIG. 15, theExLCD phantom includes nine distinct contrast sets, 1502, 1504, 1506,1508, 1510, 1512, 1514, 1516, 1518. Each contrast set, itself, includesnine objects, which are herein referred to as “pins.” The pin sizes arechosen to generate uniform samples along a logarithmic contrast levelaxis 1520. The uniform samples are derived by the following formulation:

Let the number of samples be N, and let V₁ and V_(N) be the first andlast elements, and ramp=1, 2, 3, . . . , N. Then V₁ and V_(N) can bewritten as:

$\begin{matrix}{V_{1} = a^{b + 1}} & (12) \\{V_{N} = a^{b + N}} & (13) \\{and} & \; \\{{b + 1} = {{\log_{a}\left( V_{1} \right)} = \frac{\ln \left( V_{1} \right)}{\ln (a)}}} & (14) \\{{b + N} = {{\log_{a}\left( V_{N} \right)} = {\frac{\ln \left( V_{N} \right)}{\ln (a)}.}}} & (15)\end{matrix}$

Solving equations (14) and (15),

$\begin{matrix}{a = e^{(\frac{{\ln {(V_{N})}} - {\ln {(V_{1})}}}{N - 1})}} & (16) \\{and} & \; \\{b = {\frac{\ln \left( V_{1} \right)}{\ln (a)} - 1.}} & (17)\end{matrix}$

Hence the equally sampled vec can be defined as

vec=a ^((b+ramp))  (18)

The contrast sets in this embodiment are designed so that the effectivesampling rate along the logarithmic contrast level axis 1520 is doublethat which is available from an individual pin. In one such design,contrast sets are interleaved. Specifically, in FIG. 15, any contrastset (except the set with the lowest contrast value) such as contrast set1510, has a smallest pin, represented by point 1522 on graph 1500, thatis positioned between the fourth and fifth pins of the contrast set withthe next lower contrast value, which, in this example, are representedby points 1524 and 1526, respectively, of contrast set 1512.

Pin sizes and specific contrast level values in an example embodimentare shown in Table 2. For each contrast level, there is an indication ofwhether that contrast level is required with the 20 cm diameter, the 40cm diameter or both.

TABLE 2 Pin No. 1 2 3 4 5 6 7 8 9 Size 2.00 2.57 3.31 4.26 5.48 7.059.06 11.66 15.00 (mm) Contrast 1 2 3 Set No. Contrast 1.0 2.41 5.8314.08 33.99 82.07 198.17 478.49 1155.35 Levels (HU) Used Yes Yes Yes YesYes Yes No No No with 20 cm diameter Used No No No Yes Yes Yes Yes YesYes with 40 cm diameter

In some embodiments, a phantom 300 is configured in accordance withTable 2 and as illustrated in FIG. 16. The varying contrast levels ofcontrast sets 1502, 1504, 1506, 1508, 1510, 1512, 1514, 1516, 1518 aredepicted by various pegs 1602 (only some of which are indicated),positioned longitudinally inside phantom 300. In FIG. 16, the middlethree contrast sets 1508, 1510, 1512 are positioned so that they can beused with both of the two diameter sections 1604 and 1606.

Representative cross-sections 1702 and 1704 for at least one embodimentare illustrated in FIG. 17. The image on the left illustrates a 20 cmdiameter cross-section 1702; the image on the right illustrates a 40 cmdiameter cross-section 1704. The phantom is configured so that there area plurality of slices with the same cross-section and contrast set. Bycombining the measurements from the multiple slices, a more accuratemeasurement of the actual contrast of the reconstructed object isobtained.

Additionally, in some embodiments, the phantom includes regions in whichnoise standard deviation and noise power spectrum can be measured. Alsoin some embodiments, the phantom includes a region and/or object tosupport measuring the system MTF.

In at least one other embodiment and referring to FIG. 18, an ExLCDphantom has a cross-section 1800. Different contrast levels are providedby pins in curvilinear contrast groups 1802, 1804, 1806, 1808, 1810,1812, 1814, 1816, 1818. In this embodiment, all contrast levels and pinsizes appear in each cross-section. Methods using this design includethose in which the noise response in the reconstruction as a function ofradius is incorporated. Two embodiments 1900 and 2000 sharing thecross-section 1800 are shown in FIGS. 19 and 20, respectively. Forclarity in both depictions, only three contrast sets are shown in eachfigure, 1902, 1904, 1906, and 2002, 2004, 2006, respectively. ExLCDphantom embodiment 1900 comprises cylindrical objects, some of which aredenoted as objects or pins 1908. Phantom pins 1908 provide consistentobjects from slice to slice that approximate or simulate axiallyoriented vessels in a patient to test a non-linear reconstructionprocessing that takes advantage of slice to slice consistency. ExLCDphantom 2000 comprises objects, some of which are denoted as objects orpins 2008, that are helical cylinders, i.e., for each such object,centers of the circular profiles in the horizontal two-dimensionalcross-sections form a helix. This format helps reduce coherence betweenslices and can be used to calibrate performance when slice to slicevariation is present and/or to facilitate testing non-linear anditerative reconstruction processing that takes advantage of slice toslice consistency. Either phantom 1900 or phantom 2000 can be providedwith a plurality of diameters. An embodiment using 20 cm and 40 cmdiameters is shown in FIG. 16.

In another embodiment of ExLCD phantom, the contrasts in the sectionsare chosen so that when a logarithm sampling in Flux Index is used, theexpected Contrast Index as computed using Equation (3) of one or morepins in a section with one diameter that will match the expectedContrast Index in one or more pins in one or more sections with anotherdiameter.

It will be recognized that not all ExLCD phantom embodiments will havethe same number(s) of different diameter sections, pins, and/or contrastgroups as the example embodiments described herein.

ExLCD Protocols

In at least one embodiment of the present invention, there are 20distinct protocol samples, which, for example, are selected between 0.09and 7,200.0 and that are uniformly distributed on a logarithmic relativeflux axis. The specific values for relative flux are shown in Table 3below along with the corresponding scan parameters and phantom diameter.

TABLE 3 Relative Flux Values for Selected Protocols Slice Thickness #Relative Flux mAs (mm) Diameter (cm) 1 0.092 5 1 40 2 0.183 10 1 40 30.275 15 1 40 4 0.549 30 1 40 5 1.099 60 1 40 6 1.832 100 1 40 7 3.29790 2 40 8 6.044 110 3 40 9 10.989 200 3 40 10 19.781 360 3 40 11 36.631400 5 40 12 63.006 430 8 40 13 60.000 60 1 20 14 40.000 20 2 20 1520.000 20 1 20 16 10.000 10 1 20 17 115.000 115 1 20 18 200.000 200 1 2019 360.000 360 1 20 20 660.000 330 2 20 21 1200.00 150 8 20 22 2160.00270 8 20 23 3840.00 480 8 20 24 7200.00 900 8 20

There are 12 distinct slices (cross-sections) of this ExLCD phantomembodiment as shown by the number of check marks (✓). Each of those 12slices could be scanned for each of the 20 protocols resulting in 240image slices. However, examination of FIG. 21 illustrates that only arelatively small subset of the 240 possible image slices is relevant.Hatched region 2102 in FIG. 21 represents the approximate coverage thatis used, i.e., the relevant contrast sets. Slice thicknesses should bemeasured to accurately determine the Flux Index, because there can bedifferences between the nominal selected slice and the true slicesensitivity profile.

Based on this analysis of this example embodiment, 44 image slices wereincluded in the ExLCD measurement process shown in Table 4.

TABLE 4 Image slices selected for ExLCD measurement processing SliceThickness Diameter # Relative Flux mAs (mm) (cm) Contrast Set 1 0.092 51 40 2 2 0.183 10 1 40 2 3 0.275 15 1 40 2 4 0.549 30 1 40 2 5 1.099 601 40 2 6 1.832 100 1 40 2 7 3.297 90 2 40 2 8 6.044 110 3 40 2 9 0.092 51 40 3 10 0.183 10 1 40 3 11 0.275 15 1 40 3 12 0.549 30 1 40 3 13 1.09960 1 40 3 14 1.832 100 1 40 3 15 3.297 90 2 40 3 16 6.044 110 3 40 3 1710.989 200 3 40 1 18 19.781 360 3 40 1 19 36.631 400 5 40 1 20 63.006430 8 40 1 21 60.000 60 1 20 1 22 40.000 20 2 20 1 23 20.000 20 1 20 124 10.000 10 1 20 1 25 10.989 200 3 40 2 26 19.781 360 3 40 2 27 36.631400 5 40 2 28 63.006 430 8 40 2 29 60.000 60 1 20 2 30 40.000 20 2 20 231 20.000 20 1 20 2 32 10.000 10 1 20 2 33 115.000 115 1 20 1 34 200.000200 1 20 1 35 360.000 360 1 20 1 36 660.000 330 2 20 1 37 1200.000 150 820 1 38 2160.000 270 8 20 1 39 3840.000 480 8 20 1 40 7200.000 900 8 201 41 115.000 115 1 20 2 42 200.000 200 1 20 2 43 360.000 360 1 20 2 44660.000 330 2 20 2

ExLCD Methods

In some embodiments of the present invention, the ExLCD detectabilitymethod includes one or more of the detection methods listed above alongwith a capability to incorporate actual measured contrast. In some ofthese embodiments, a pin image contrast is measured as follows:

1. calibrate the phantom to determine the effective mean contrast of thepins;2. use the calibrated phantom images to define a map of the pixellocations within the geometric area of each pin;3. use the pin area maps to measure the average contrast for each testcondition; and4. Average the value from multiple slices that are identical in theirgeometry and contrast set.

Referring now to FIG. 22, a result of one embodiment of thedetectability determination for a reconstructed image slice 2200described as Test 32 in Table 4 is shown. The smallest pins 2202, 2204,2206 detectable in each of three contrast sets are identified asindicated in chart 2208. Based on the identified pin numbers, thecorresponding pin sizes 2202 and 2204 and the associated contrast levels2210 and 2212, respectively, comprise the raw data for the measurementfor that reconstructed image 2200.

For example, three ExLCD contrast measurements are recorded using thedefinition written as Equation (19).

$\begin{matrix}{\left\lbrack {\frac{6000}{9.06 \times 14},\frac{6000}{2.56 \times 34},\frac{6000}{2.0 \times 82}} \right\rbrack = {\left\lbrack {47,69,37} \right\rbrack.}} & (19)\end{matrix}$

For this example, the smallest (2.0 mm) pin is not carried onto theExLCD contrast measurement plot because it is assumed that there is noway to verify that it is the smallest pin detectable. Therefore, and asshown in FIG. 22, the first two contrast measurements, [47,69] in chart2200 are carried onto the ExLCD contrast measurement plot 2214 at theFlux Index location (10.0) indicated for Test 32 in Table 4. In FIG. 23,the collection of ordered pairs is shown along with a ContrastPerformance Curve that is a regression fit to the collection of orderedpairs.

ExLCD Graph

The range of flux index for at least one known CT scanner isapproximately [0.1, 7,000.0]. A corresponding range of contrast index isapproximately [0.5, 1000.0]. These ranges define the range orcorresponding ranges for other CT scanners of an ExLCD graph. Referringnow to graph 2400 of FIG. 24 in log-log format, arrows A, B, C, D, and Egenerally point in directions of better image quality, lower technique,larger patients, smaller objects (lower contrast) and larger objects(higher contrast), respectively.

Larger values of Contrast Index indicate better image quality or theability to detect smaller, lower contrast objects. Smaller values ofContrast Index indicate poorer image quality or the ability to detectonly larger, higher contrast objects.

Larger values of Flux Index indicate higher dose or smaller patientsizes. Smaller values of Flux Index indicate lower dose or largerpatient sizes.

ExLCD Detectability

Various ExLCD process embodiments can incorporate any combination ofdetectability methods listed above, one of which, for example, isrepresented by chart 2500 of FIG. 25. Block 2502 is an average image ofmultiple scans of the phantom objects. The average reduced the noise sothat the pixels represent the object, and block 2504 is a relativelynoise-free representation of the object. The ratio of contrast of object2512 extracted from the image, relative to the contrast of the inputobject 2510, is the object contrast reduction factor (OCRF). In block2506, the noise in a uniform region of the image is filtered byconvolution with a kernel made with block 2504. The resultingdistribution of the filtered noise pixels is offset by 3 standarddeviations divided by the OCRF to determine the contrast thresholdrequired to claim detectability.

Single or multiple observer methods may be used to determinedetectability within the ExLCD process. For example, in one embodiment,each human observer examines each of the images to assess thedetectability for each pin within the contrast sets.

Results of multiple human observers analyzing various ExLCD experimentsdemonstrate that there is a wide variation in results among humanobservers. In fact, the variation among observers is large compared toexpected measurement variations among CT scanners.

A known statistical method from a single protocol LCD method is suitablefor use in one embodiment of an ExLCD process. The known prior artstatistical method is described, for example, in Computed Tomography:Principles, Design, Artifacts and Recent Advances, Jiang Hsieh,Copyright 2003 by the Society of Photo-Optical InstrumentationEngineers, Bellingham, Wash., and is a variation of the ContrastDiscrimination Factor (CDF) described in the international standard ASTME1695-95, “Standard Test Method for Measurement of Computed Tomography(CT) System Performance.” The algorithm as described therein is appliedto each of 44 images generated in one example embodiment of the ExLCDprocess. The smallest pin in any contrast level that achieves thebackground separation is selected for that contrast set. Thus, if theideal contrast value is at or above the noise standard deviation forthat pin size, the contrast measure for that pin and that contrast levelis placed onto the ExLCD contrast measure graph as illustrated in FIG.26.

The statistical method generates the most consistent contrastperformance curves. However, the statistical method tends to bias allresults toward higher contrast measures and it cannot generate accuratecontrast measures when non-linear or iterative reconstruction is used.

The “Rose criterion” has long been a robust standard for imagedetectability analysis of low contrast objects embedded in a white noisebackground. The Rose Criterion Derivation is another prior art methodsuitable for use in some embodiments of the present invention. The RoseCriterion Derivation relates object size, measured object contrast, andbackground noise (i.e., pixel standard deviation) in a formula thatestablishes a detectability index v written as:

$\begin{matrix}{v = {\frac{Cp}{\sigma} \times \frac{\sqrt{}}{2S}}} & (20)\end{matrix}$

where C is the measured object contrast,p is the pin diameter,S is the image pixel size, andσ is the measured standard deviation of the background noise.Note that in equation (20), the measured contrast level is indicatedwith an upper case C, differentiating it from the nominal contrast levelof Equation (10), indicated with a lower case c.

Detectability values are computed for each of the contrast levels foreach of the 44 image slices available in this example embodiment. Thedetectability values that are at or above the detectability thresholdare flagged as “detectable.” Although known Rose Criterion derivationssuggest a threshold of 4, we have determined that a threshold of 5 ismore consistent with human observer results. The selected detectabilityvalues are shown in plot 2700 of FIG. 27. For each detectable pin, anExLCD Contrast Index value is determined and that value is plotted asshown on ExLCD contrast measure graph 2800 shown in FIG. 28.

Known Rose criterion definitions rely on measured contrast to determinea detectability index. However, we have investigated the behavior of theRose detectability method when ideal or nominal contrast is used insteadof the measured contrast. We have found that such a Rose-Idealdetectability index can then be written (note the use of the lower casec):

$\begin{matrix}{v_{1} = {\frac{cp}{\sigma} \times \frac{\sqrt{}}{2S}}} & (21)\end{matrix}$

The Matched Filter detectability method relies upon a formulation for anIdeal Bayesian Observer (IBO). An ideal observer is one whose dataanalysis performance is the highest possible. The Matched Filterdetectability method uses a formulation of the IBO ideal decision makerwritten as

$\begin{matrix}{{{SNR}^{2} = {K^{2}{\int{\frac{{{f(\tau)}}^{2}{{MTF}^{2}(\tau)}}{W_{n}(\tau)}d\; \tau}}}},} & (22)\end{matrix}$

where f is the Fourier transform of the ideal object,K is the large area transfer factor,MTF is the system Modulation Transfer Function (MTF), andW_(n) is the noise power spectrum.

In Equation (22), the term |f(τ)|² MTF²(τ) is effectively the powerspectrum of the reconstructed object with no noise. This formulationworks for a linear, shift-invariant system but may not be adequate formodeling non-linear reconstruction methods. In order to generalizeEquation (22) for the non-linear case, we replace the term |f(τ)|² MTF²(τ) with |{circumflex over (f)}_(o)(τ)|², the power spectrum of theobject-dependent reconstruction of an ideal object, o.

Thus,

$\begin{matrix}{{SNR}_{o}^{2} = {K^{2}{\int{\frac{{\hat{f_{o}}(\tau)}^{2}}{W_{n}(\tau)}d\; \tau}}}} & (23)\end{matrix}$

and the Matched Filter detectability index, v_(o), is written

$\begin{matrix}{v_{o} = {{SNR}_{o} = {K{\sqrt{\int{\frac{{{\hat{f_{o}}(\tau)}}^{2}}{W_{o}(\tau)}d\; \tau}}.}}}} & (24)\end{matrix}$

The object, o, is “visible” if v_(o) is greater than a predeterminedthreshold. The NPWMF and NPWEFMF are examples of matched filters, thelatter incorporating an additional term modeling frequency response ofthe human eye.

FIG. 29 is an illustration representing the Matched Filter method, usingimages and graphics as illustration aids. A reconstructed image noisefield 2902 is convolved with an ideal reconstructed image 2904 of a pinto produce a filtered noise field 2906. A sequence 2908 of (for example)fifteen ideal reconstructed pins is combined with the filtered noisefield 2906 to produce an image 2910. Image 2910 is used to determine acontrast amplitude necessary to achieve detectability above a specifiedthreshold.

Computing v_(o) uses an overall constant K that is implicit in someembodiments of our ExLCD process. Constant K does not vary with the CTscanner used for imaging, but is used to force numbers into acomputationally convenient range so that they may be manipulatedefficiently by computational hardware and software (such as computersand/or special modules) that are used or that comprise some embodimentsof the present invention. The noise power spectrum, W_(n), is computedas a radial average of the 2D Fourier transform of a large uniform noiseregion of pixels. This region should be highly uniform and is preferablyfree from even minor cupping, bands or rings. The result is scaledappropriately for pixel size and number of pixels.

The object-dependent Fourier transform of the object, |{circumflex over(f)}₀(τ)|², is computed as a radial average of the 2D Fourier transformof the reconstructed object. The small region of pixels containing theobject is preferably selected to reduce noise contamination. As with thenoise power spectrum, the result is preferably scaled appropriately forpixel size and number of pixels.

ExLCD Performance Function

As described above, the output of any of the detectability methodsapplied to the 44 image slices in some of the example embodiments is acollection of ordered pairs [Flux Index, Contrast Index] that correspondto the smallest pins that are “detectable” for applicable contrastlevels. In some embodiments of the present invention, this collection ofordered pairs can be plotted on a log-log scale as shown FIG. 30 andthen used to build the ExLCD Performance Function. Data points are thenfit to a 2-parameter equation that includes quantum detection efficiencyand system/electronic noise. As an example, FIG. 31 illustrates acontrast performance curve 3102 generated in one embodiment by aleast-squares fit.

In the absence of non-linear reconstruction methods, it can be shownthat the ExLCD Contrast Index is approximately proportional tosignal-to-noise using a relationship written as

$\begin{matrix}{{M \cong {K \times \frac{\rho \; J}{\sqrt{{\rho \; J} + \sigma_{e}^{2}}}}},} & (25)\end{matrix}$

where [J, M] represent the ordered pairs, [Flux Index, Contrast Index],ρ corresponds to the Contrast Gain Factor, andσ_(e) corresponds to the standard deviation of the system/electronicnoise.In some embodiments, for each collection of ordered pairs, values for ρand e are determined that best fit the measured ordered pairs.

In some embodiments of the present invention, parameters p and σ_(e)provide a definitive characterization of a CT scanner. To illustratethis definitive characterization, the Results and Experiments sectionincludes results showing how different detectability methods react tospecific scanner changes that affect ρ and σ_(e).

A scanner has better performance when the ExLCD process reports highervalues for contrast gain and lower values for electronic noise. Forexample, comparison plot 3200 of FIG. 32 shows that Scanner 2 has ahigher (better) contrast gain than Scanner 1, Scanner 3 has a somewhatlower (worse) contrast gain than Scanner 2, and Scanner 3 has lower(better) electronic noise than Scanner 1.

Referring now to FIGS. 33 and 34, when a smaller pin is blurred by thesystem MTF, there may be a corresponding reduction of contrast. That is,a highly filtered noise spectrum with a highly filtered object resultsin a lower detectability score than a reconstruction process (e.g.non-linear reconstruction) that results in a highly filtered noisespectrum but which is capable of retaining the spatial geometry of theoriginal object. This phenomenon is typically observable and measurableonly for the smaller contrast pins. Therefore, in some embodiments, theExLCD method uses a small pin performance curve, estimated from thecontrast measurements involving the pins that are impacted by the MTF.Referring now to FIGS. 35 and 36, contrast measures 3502 and 3602 areshown for large pins (upper) and small pins (lower), respectively. Acomparison of the contrast performance curves for a large pin vs. asmall pin is shown in FIG. 36.

Phantom Calibration

Physical ExLCD phantoms will have some engineering variability that willcause each of them to deviate somewhat from an ideal phantom design.Therefore, in some embodiments of the present invention, the ExLCDprocess compensates for this variability by incorporating a calibrationcomponent that determines and records actual contrast values and actualpin location values. The actual contrast values, determined by thecalibration, are then used as the nominal contrast values c for allExLCD measurements in that embodiment. The use of actual pin locationvalues improves the accuracy of measured contrast values C fornon-observer detectability determinations. Referring again to FIG. 5, inone phantom embodiment 300, there are large wedge shaped regions 502 ofmaterial to facilitate computation of actual contrast values.

The calibration component effectively compensates for x-ray spectralvariations between scanners. Also, the calibration component includes aphantom manufacturing tolerance check. If the phantom slices are out oftolerance in contrast, pin size or pin locations, some embodiments ofthe present invention report the fact that the phantom slices are out oftolerance and/or the difference between the actual and nominal locationvalues.

Some embodiments of the present invention use a Channelized HotellingObserver (CHO) detectability metric. These metrics are used as an IQgoal to improve control of a radiographic imaging system and to minimizeor at least reduce the problems listed above for IQ goals used in knownradiographic imaging systems. In some embodiments of the presentinvention, ExLCD is incorporated into a CT AEC system. In yet otherembodiments, ExLCD is used to obtain desired IQ goals by externallyrecommending required settings to use for patient scanning.

The user of a radiological imaging system such as a CT scanner selects aset of scan and reconstruction parameters, known as a protocol, forscanning a patient. The slice thickness, mAs settings and patientattenuation influence the amount of x-rays used to produce the image.Fewer x-rays increase noise and result in a poorer quality image. Thequality of the image is also dependent on the selection of kVp, sourcefiltration, collimation, and image reconstruction parameters. Tocharacterize the performance of the scanner, a phantom with severaldiameters covering the typical range of patient sizes (a range of abouta 10 cm to 45 cm water equivalent diameter) is needed in someembodiments. Referring again to FIGS. 3, 4, 5, and 6, each phantom 300diameter section 302, 304, 306, 308 contains a set of low contrastobjects such as rods 402, a uniform background region 602, and a set oflarge low contrast regions such as wedges 502. The low contrast valuesof the objects are chosen to be near the visual limit of detectabilityfor the diameter in which the object is located. The low contrast valuesare increased for the larger diameter sections to account for thequantum noise increase with increasing attenuation. If phantom 300 isconstructed using 3-dimensional printing methods, discrete diameters canbe replaced by one or more conical sections where the object contrastsmay also continuously increase with increasing effective conicaldiameter. Three-dimensional printing methods allow very complex phantoms300 to be constructed. For example, anthropomorphic phantoms using CTimages as input could be printed. This would allow CHO to be used toevaluate the detectability of realistic lesions in an anthropomorphicbackground instead of simple geometric objects in a uniform background.

Although phantom materials are chosen to be as energy independent aspossible, the actual contrast of the objects will change depending onthe effective energy of the imaging system. Ideally, the large lowcontrast regions should be made of the identical material as the lowcontrast objects to allow the contrast produced by the imaging system tobe measured.

In one embodiment and referring now to flowchart 3700 of FIG. 37, aphantom 300 is scanned at a range of doses for a core operating mode atblock 3702. A core operating mode is the set of all conditions ofoperation except those typically used to control x-ray intensity such asmAs or an image quality goal for an auto exposure control (AEC)protocol. Scans are obtained at a sequence of different dose levelsextending over the range of settings provided by the scanner. Scans arerepeated to produce a sufficient number of images to train and evaluatescanner performance using a Channelized Hotelling model Observer (CHO).In some embodiments, about 300 object-present and 300 object-absentimages for each object instance. An object instance is a unique objectsize, such as rod diameter, and contrast in the image.

In some embodiments, a Channelized Hotelling model Observer (CHO) moduleimplemented in hardware or software or some combination thereofdetermines a signal to noise SNR for each object instance within phantom300. CHO is considered to be the most advanced class of model observer.CHO is currently considered to be the model observer that is the mostpractical and accurate predictor of human performance in detecting anobject {Myers 1987} {Barret 2004}. CHO produces an SNR and statisticaldistributions for object present and object absent trials. Hence, inaddition to SNR, the object present and object absent distributions canbe used to generate a Receiver Operating Characteristic (ROC) curve. TheROC curve is a plot of true positive fraction (TPF) vs false positivefraction (FPF). A typical measure for an ROC curve is the Area Under theCurve (AUC).

A regression model of detectability results from block 3704 is generatedand stored by a computer or computational engine at block 3706. Theresults and/or distributions thereby obtained are reported to a human(e.g., by a display device or print-out) and/or stored in memory and/ora digital medium (such as a CD, DVD, RAM, or ROM) at block 3708. Patientimages representing a desired clinical image quality (IQ) are selectedat block 3710, and, in conjunction with the regression model ofdetectability results obtained at block 3706, the detectability ofdesired patient images is determined at block 3712. A desireddetectability performance function so obtained is then used at block3714 to lookup and set (in some embodiments, automatically viaelectronic circuitry) conditions of operation to produce a desiredresult on a patient.

As shown in FIG. 38, CHO provides an output of Signal to Noiseperformance for each instance of pin size and contrast. The SNR resultsfrom a model observer method, such as CHO, is a set of discrete valuesas a function of FluxIndex for each object instance. In someembodiments, CHO is provided as a hardware or software module thatperforms the CHO model observer method. An interpolation (for example,by a regression model) of the SNR results in a function of FluxIndex,for example, provides a continuum of results (an ExLCD performancefunction) that represent the SNR performance of a scanner and/or allowsimage quality results to be duplicated on another scanner. Referring nowto FIG. 39, the desired patient CIQ (SNR) 3902 is determined frompatient images or by statistical methods of reviewing distributions ofpatient CIQ results. Typically the desired CIQ is a function of patientsize. The patient size in terms of a water equivalent diameter D_(weq)is determined for the patient image {Menke 2005} and the conditions ofoperation from a DICOM header determine the FluxIndex 3904 of a firstscanner performance function 3906 for a core operating mode, which inturn defines the SNR value from the performance function of the firstscanner on which the patient images with the desired CIQ were obtained.Using the performance function 3908 from a second scanner, the desiredSNR and patient D_(weq) indicate the desired mAs 3910.

Performance functions for a scanner using an auto exposure control (AEC)mode are determined in a similar manner but are organized as acollection of performance functions 4002, 4004, 4006, 4008 vs. patientsize as shown in FIG. 40.

Several methods for mapping the SNR performance functions from a modelobserver such as CHO are discussed in the following. SNR performancefunctions for each object instance can be mapped directly, as anaggregate one-dimensional SNR performance function, or converted to acontrast index for use in ExLCD.

Method 1—Direct Mapping of Multiple SNR Instance Functions

Referring now to FIG. 38, individual SNR performance functions for eachobject instance comprise an embodiment of scanner characterization. Adesired FluxIndex and associated SNR values of a first scanner in theexample of FIG. 38 are calculated by a computing engine or module (forexample), using the patient D_(weq) and scanner settings of the clinicalpatient images that were determined to be clinically acceptable.Referring now to FIG. 41, the desired FluxIndex 4102 on the firstscanner intersects a collection of SNR values 4104, 4106, 4108 onperformance functions 4110, 4112, and 4114, respectively, of the firstscanner. The collection of SNR values for the first scanner aretranslated to the second, different scanner and the associatedcollection of FluxIndex values 4116, 4118, and 4116, respectively, arecombined (for example, by using a weighted average) to provide thedesired FluxIndex and associated protocol settings for scanning apatient.

The weighting of members of the collection is dependent on thediagnostic task. For example, if the task is to look for liver lesions,the SNR values of the lower contrast pins having a diameter similar tolesions of diagnostic interest would be selected or weighted strongerthan the SNR for objects less relevant to the diagnostic task. Anotherdiagnostic task requiring higher spatial resolution might have increasedweighting for the smaller diameter pins. The appropriate weightingscould be determined by skilled radiologists.

Method 2—Mapping an Aggregate SNR Function

In one embodiment and referring now to FIG. 42, an aggregate SNRfunction is generated. A reference contrast is selected such as 8 HU.Each instance SNR is adjusted by the ratio of the reference contrastrelative to the contrast of the object instance. A combination (forexample, a weighted mean) of resulting SNRs for the different objectdiameters is determined to provide one-dimensional SNR performancefunctions 4202, 4204, using a suitable computer or computing engine. Analternative is to adjust each instance SNR by the ratio of a referenceobject diameter times a reference contrast divided by the product of theobject instance diameter times its contrast. An aggregate weighted meanfunction 4206 is generated in some embodiments.

Method 3—Mapping SNR to a Contrast Index Using an Object PresentThreshold

In some embodiments and referring now to FIG. 43, combinations (forexample, made by regression models) of multiple object instances areused to find a FluxIndex required for each pin contrast in order toachieve a specified SNR. For example, an SNR value of 5 is chosen as adetectability threshold 4302 and 5 mm diameter pins are selected. Theassociated FluxIndex values 4304, 4306, 4308, 4310 required to producethat SNR for each contrast are determined from plot 4312. FluxIndexvalues for an SNR value of 5, and the pin contrast and diameters arethen used to determine the contrast index at each FluxIndex 4304, 4306,4308, 4310 FluxIndex as shown in plot 4314. For example, referring toplot 4312, the 5 mm, 128 HU pin requires a FluxIndex of 10 for an SNR of5. Plot 4314 is then used to find the contrast index of 9.375(6000/5×128) at the FluxIndex of 10. This is done for all of the otherpins to obtain a set of points describing the FluxIndex needed for eachobject to achieve an SNR of 5. In some embodiments, combinations ofthese data (for example, made by a regression model) are computed todescribe the contrast index vs. FluxIndex for a given diameter pin, 5 mmfor example, as shown in plot 4314.

In some embodiments and referring to FIG. 44, this process is done forall pin diameters, and a combination (for example, a weighted mean) ofthe different pin diameter contrast index functions is used to define asingle contrast index function 4402 for guiding clinical practice asdescribed elsewhere herein.

Method 4—Mapping SNR to a Contrast Index Using an ROC Curve

Referring now to FIG. 45, instead of arbitrarily selecting an SNRdetection threshold, some embodiments of ExLCD applications usestatistical distribution information from a CHO ROC curve 4508 todetermine detectability. For example, a smallest detectable pin isselected from the set of pins at a given FluxIndex. To define an objectas detectable, some embodiments use a desired AUC threshold, forexample, AUC>0.95 above which an object is deemed detectable. Thisdefinition is possible because CHO provides distribution information4504, 4506 as well as the SNR to allow the AUC to be calculated from theROC curve 4508 of each pin. This embodiment is especially useful in atleast some instances in which the probability distribution functionsboth with and without object present are non-normal as a result of aniterative reconstruction process, for example.

Presentation of SNR Information for Comparing Scanners and OperatingModes

Data from a CHO analysis can also be presented in a variety of ways asindicated in FIG. 46 to allow a comprehensive comparison of thedetectability performance of different scanners and operating modes. Forexample, the SNR for a given size object as a function of FluxIndex inFIG. 38 is given as a function of

-   -   mAs at a specified D_(weq),    -   D_(weq) at a specified mAs, as shown in plot 4602.

Since CTDIvol (the standard CT Dose Index) is associated with mAs forthe core operating mode, CTDIvol can be substituted for mAs, allowingSNR to be presented, as shown in plot 4604 as a function of:

-   -   CTDIvol at a specified D_(weq),    -   D_(weq) at a specified CTDIvol.

Other possible presentations are

-   -   CTDIvol vs. D_(weq) at a specified SNR,    -   mAs vs. D_(weq) at a specified SNR.

These and other presentations of the CHO data can provide previouslyunknowable insight into performance of scanner features andcapabilities. Generating CHO data and organizing it in various ways toprovide a continuum (for example, using regression functions) allowsthis unique probing of the IQM vs. dose performance of radiographicimaging devices, such as a CT scanner.

Incorporation of ExLCD in an Auto Exposure Control (AEC) System

ExLCD can be incorporated into a CT scanner AEC system and thereby usethe ExLCD contrast index as the image quality goal to guide clinicalpractice. While current AEC system IQ goals are relative IQ modelsrestricted to a scanner make or model (such as noise standard deviation,quality effective mAs or a reference image), ExLCD is universal andeliminates the confusion of different manufacturer's parameters forcontrolling AEC. ExLCD also allows a clinical database of contrast indexvalues determined as standard of care by a large number of clinicians tobe employed on any scanner with an ExLCD characterization.

Since the asymmetry ratio (AR) is determined, ExLCD could also be usedto control the angular modulation in an AEC system.

Using ExLCD on a Scanner with an Existing AEC System

In some embodiments and referring again to FIG. 40, ExLCD can also beadapted to provide contrast index results for an existing AEC system. Asshown in FIG. 40, contrast index functions 4002, 4004, 4006, 4008 aremeasured and obtained as a function of the ExLCD phantom waterequivalent diameter for a set of Image quality goal parameters providedby the CT AEC system. When scanning patients, the desired contrast indexfor the diagnostic task and patient size (D_(weq)) identify theappropriate AEC IQ goal to use for scanning the patient.

In some embodiments and referring to flowchart 4800 of FIG. 48, ExLCD isembedded in modules in a CT scanner. An IQM 4706 corresponding to theCIQ required for a selected clinical task 4804 is determined by clinicalresearchers 4806 or locally to reduce dose variance. IQM 4706 is givento a technologist 4808 who enters the ExLCD goal 4706 for a scanningsystem 4810 having automatic exposure control. The AEC system of scanner4810 then determines parameters with which to perform a scan of patient4812. Scanner 4810 and its AEC system thus produce consistentlyacceptable images 4814 at a low (or even at the lowest possible) doseconsistent with producing an image suitable for the selected clinicaltask 4804.

In some embodiments and referring now to flowchart 4900 of FIG. 49,ExLCD is provided as an external advisor to CT scanner 4810. Differencesbetween these embodiments and those represented by FIG. 48 (whichembodiments are not necessarily exclusive of one another; i.e., anembodiment can have both embedded ExLCD and external ExLCD) include thatIQs 4802 in the embodiments represented by FIG. 49 are sent or enteredinto an external computer or computational engine 4902. Also, CTradiographs 4904 may be sent or entered to computer or computationalengine 4902 for scan planning. Scan parameters, including one or moreprovided by computer or computational engine 4902 are sent to (or readby) technologist 4808 who then enters these scan parameters directlyinto scanner 4810 rather than entering an ExLCD IQ goal. Examples ofsuch parameters include Kv, scan time, bowtie filter, reconstructionalgorithm, and post-processing algorithm.

Although computer 4902 is shown as a handheld touchscreen device in FIG.49, many other types of computers are suitable for use in variousembodiments and more generally throughout the various embodiments ofinventions described herein. For example, with regard to computer 4902shown here, a desktop or laptop computer is also suitable, as well asspecial purpose computers and single purpose computers. The computersneed not be portable and also can include computers with physical and/orsoftware security.

More generally, it is a design choice whether, in any particularembodiment, a computer or computer engine is a separate entity from ascanner, included within the scanner, or a separate module or modulesthat are or are not located within a scanner.

One of ordinary skill in the art will thus appreciate that someembodiments of the ExLCD process are capable of successfullycharacterizing the contrast performance of a CT scanner over its entireflux range. Also, ExLCD processes are adaptable to other radiographyapplications such as digital radiography, mammography, nuclear medicineand SPECT.

In at least one known LCD process, a single LCD measurement provides noinformation about the contrast performance of a scanner in the lowerflux regions including (1) body scans at lower dose, (2) scans for alarge body, and (3) fast scans.

One of ordinary skill in the art will now appreciate that, without theExLCD process, human observer detectability determination is lessconsistent than either of the automatic methods, namely, statistical andRose. In fact, observer detectability determination, by itself, is notaccurate enough to differentiate the contrast performance among typicalcommercial scanners.

It will further be appreciated by those skilled in the art that variousapparatus and method embodiments of the present invention provide aperformance function for a radiographic imaging system (such as CT) thatcharacterizes detectability over the operating range of the system. Insome embodiments, a performance function is provided that can beassociated with clinical performance related to dose utilization.

The ExLCD embodiments described herein are particularly adapted forautomated forms of implementation. For example, ExLCD methods may beimplemented using a general purpose computer or by a specially designedapparatus. The use of a specially designed apparatus is preferred, inthat a specially designed apparatus can provide greater security in, forexample, a clinical setting as well as simplified controls for atechnician to operate and the ability to control a plurality of scannersto provide consistently acceptable images at low doses.

Some methods and apparatus embodiments of the present invention are alsouseful in conjunction with non-linear and iterative image reconstructionmethods.

A special phantom or set of phantoms can be used with a large array ofobjects of various sizes and contrasts designed to cover the range oflowest to highest possible flux conditions.

Referring now to FIG. 47, embodiments of ExLCD methods and apparatus canbe enhanced by using all object sizes for a given contrast level todetermine a smallest detectable object. An improvement of accuracy ofdetection of the “smallest pin size” is thus obtained by fitting thepoints to a line and determining where the fitted line crosses thedetectability threshold. In other embodiments, an enhancement is made byusing all object contrasts for given object size. In FIG. 47, “x” points4702 indicate detectability values for each object size for a givencontrast level. Line 4704 is a linear fit of the detectability values4702. Location “A” indicates the smallest object size based on thesmallest distinct object above a detectability index threshold 4706.Location “B” indicates the smallest object size based on a fit using allobject sizes.

A detectability calculation analyzes each object and noise spectrum forsets of objects within the band of contrast levels encompassing thethreshold of detectability. In some embodiments, the detectabilitycalculation uses a Non Pre-whitening Matched Filter Signal to Noiseratio in which the object signal is reduced by the object contrastreduction factor.

It will be appreciated that some embodiments of the present inventionprovide a performance function that can be used to reproduce clinicalperformance for any patient on a scanner that has been characterized.The performance function provides an objective quantifiable scoringscale for qualitative clinical imaging.

In some embodiments of the present invention, the minimum clinical imagequality scores can be determined and assigned for various clinicalproblems by medical researchers. For a particular patient and clinicalproblem, these scores can be used to determine the precise conditions ofoperation required for a characterized scanner for a particular scan.

It will be appreciated that some embodiments of the present inventionprovide at least one or more desirable features, among which may includecharacterization of the performance of a radiometric imaging apparatussuch as a CT scanner at more than one protocol, over a full operatingrange of the imaging apparatus, or both. Also included may be theadequate handling of smaller pins that are affected by system blurringand/or remedying of the inadequacy of a single protocol contrastperformance curve. Also included may be the remedying of inaccuraciesthat prevent true differentiation of contrast performance betweendifferent CT scanners, an adequate description of CIQ, a universaldescription of CIQ, and the tracking of desired CIQ with patient size.In addition, some advantages that may be realized include less confusionamong technologists, and a better way to determine detectability inradiometric imaging systems.

While the invention has been described in terms of various specificembodiments, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theclaims.

REFERENCES

-   {Barrett 2004} Barrett H H, Myers K J, Foundations of Image Science,    2004 John Wiley & Sons.-   {Menke 2005} Jan Menke, M D, “Comparison of Different Body Size    Parameters for Individual Dose Adaptation in Body CT of Adults”,    Radiology 2005; 236:565-571.-   {Myers 1987} Myers, K. J and Barrett, H. H. “Addition of a channel    mechanism to the ideal observer model”, Journal of the Optical    Society of America, Vol. 4, Issue 12, pp. 2447-2457.-   {AAPM 2011} AAPM Report 204, 2011 “Size Specific Dose Estimates    (SSDE) in Pediatric and Adult Body CT Examinations.

1-16. (canceled)
 17. A device comprising: a least one data processorassociated with at least one radiographic image acquisition system; aninput operable to receive base relationship data representative of amathematical relationship derived from a plurality of flux levels of aplurality of radiographic phantom images; the at least one dataprocessor operable to associate image quality metrics correspondinglywith flux levels in accordance with the base relationship data; a memoryoperable to store the base relationship data; an input operable toreceive image quality selection data corresponding to at least oneselected image quality metric; the at least one data processor furtheroperable to calculate a unique dose control signal at least oneradiographic imaging operation in accordance with application of theimage quality selection data to corresponding, pre-associatedrelationship data; and an output operable to relay each dose controlsignal to an associated one of the one or more radiographic imagers. 18.The device of claim 17 wherein the radiographic imaging system comprisesa CT scanner.
 19. The device of claim 18 wherein the CT scanner isoperable in a plurality of imaging modes.
 20. The device of claim 17wherein the relationship data is comprised of a data relationship table.21. The device of claim 17 wherein the at least one processor is furtheroperable to generate the relationship data in accordance with apreselected relationship curve.
 22. The device of claim 21 wherein therelationship curve is associated with a predefined function.
 23. Thedevice of claim 17 wherein the image quality metric is associated withdetectability of at least one physiological state.
 24. The device ofclaim 17 wherein the image quality metric is associated with at leastone image artifact.
 25. The device of claim 17 wherein the image qualityselection data is comprised of a relationship between image quality andpatient size.
 26. The device of claim 25 wherein patient size iscomprised of a water equivalent diameter.
 27. A method for evaluatingdose performance of a radiographic imaging system with respect to imagequality using a phantom and a model observer method comprising: scanningand producing images for a plurality of sections of the phantom usingthe radiographic imaging system, wherein the plurality of sectionsrepresents a range of patient sizes and doses and wherein the pluralityof sections of the phantom contain objects of measurable detectability;analyzing the images to determine observability for one or more of theobjects within the images of the plurality of sections of the phantom,wherein said analyzing comprises using a model observer; and outputtinga continuous image quality performance measurement function usingdetermined observability results.
 28. The method of claim 27 wherein themodel observer is based on a detectability analysis.
 29. The method ofclaim 27 wherein said outputting further comprises outputting thecontinuous observability performance measurement function as a functionof Flux index to obtain a family of observability performancemeasurement functions.
 30. A radiographic imaging system wherein imagingsystem performance of a first radiographic imaging system is reproducedin a second radiographic imaging system imaging comprising: an inputoperable to receive base relationship data representative of amathematical relationship derived from a plurality of flux levels of aplurality of radiographic phantom images from a first radiographicimaging system; an input operable to receive base relationship datarepresentative of a mathematical relationship derived from a pluralityof flux levels of a plurality of radiographic phantom images from asecond radiographic imaging system; a processor configured to translateat least one image quality measure value at a flux index value of thefirst radiographic imaging system to at least one image quality measurevalue at a flux index value of a second radiographic imaging systemusing at least one performance measurement function of both the firstradiographic imaging system and the second radiographic imaging system;and the processor configured to calculate a dose control signal toproduce said imaging performance on the second radiographic imagingsystem using at least one flux index value of the second radiographicimaging system.
 31. A method comprising: reproducing the imagingperformance of the first imaging system using a second imaging systemand a performance measurement function of the first imaging system, saidreproducing including determining an image quality measure value on aperformance measurement function of the second radiographic imagingsystem and an associated flux index to calculate settings of the secondradiographic imaging system for reproduction of said performance. 32.The method of claim 31 wherein the image quality measure is comprised ofa detectability-based measure.
 33. The method of claim 31 wherein afamily of image quality measure values at a flux index of the firstradiographic imaging system is translated to a family of image qualitymeasure values at a flux index of a second radiographic imaging systemusing performance measurement functions of both the first radiographicimaging system and the second radiographic imaging system.
 34. Themethod of claim 33 further comprising displaying the family ofperformance measurement functions as a function of mAs at a fixedpatient water equivalent diameter D_(weq).
 35. The method of claim 33further comprising displaying the performance measurement functions as afunction of standard dose index CTDIvol at a fixed patient waterequivalent diameter D_(weq).
 36. The method of claim 33 furthercomprising displaying the performance measurement functions as afunction of patient water equivalent diameter D_(weq) at a fixed mAs.